Triple

T16861947
Position Surface form Disambiguated ID Type / Status
Subject Sergius of Radonezh E409934 entity
Predicate associatedWithPlace P2830 FINISHED
Object Zagorsk E454063 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Zagorsk | Statement: [Sergius of Radonezh, associatedWithPlace, Zagorsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zagorsk
Context triple: [Sergius of Radonezh, associatedWithPlace, Zagorsk]
  • A. Zagorsk chosen
    Zagorsk is the former Soviet-era name of the Russian town now known as Sergiyev Posad, a major center of Orthodox Christianity northeast of Moscow.
  • B. Pavlovo
    Pavlovo is a historic town in Russia, known as an administrative center and for its traditional metalworking and handicraft industries.
  • C. Pavlovo
    Pavlovo is an urban-type settlement located within the Kirovsky District of Leningrad Oblast in northwestern Russia.
  • D. Karlovo
    Karlovo is a historic town in central Bulgaria, known as the birthplace of national hero Vasil Levski and as a gateway to the Balkan Mountains.
  • E. Novi Grad
    Novi Grad is the fictional capital city of the Eastern European nation Sokovia in the Marvel Cinematic Universe.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b5036e6c8190a3b9e525a34da2e1 completed April 18, 2026, 4:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb274ba48190951acde0821e05a0 completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:24 a.m.